BERT medium (cased) model trained on a subset of 125M tokens of cc100-Swahili for our work
Scaling Laws for BERT in Low-Resource Settings at ACL2023 Findings.
The model has 51M parameters (8L), and a vocab size of 50K.
It was trained for 500K steps with a sequence length of 512 tokens and batch-size of 256.
Results
Authors
Gorka Urbizu [1], Iñaki San Vicente [1], Xabier Saralegi [1],
Rodrigo Agerri [2] and Aitor Soroa [2]
Affiliation of the authors:
[1] Orai NLP Technologies
[2] HiTZ Center - Ixa, University of the Basque Country UPV/EHU
Licensing
The model is licensed under the Creative Commons Attribution 4.0. International License (CC BY 4.0).
Acknowledgements
If you use this model please cite the following paper:
- G. Urbizu, I. San Vicente, X. Saralegi, R. Agerri, A. Soroa. Scaling Laws for BERT in Low-Resource Settings. Findings of the Association for Computational Linguistics: ACL 2023. July, 2023. Toronto, Canada
Contact information
Gorka Urbizu, Iñaki San Vicente: {g.urbizu,i.sanvicente}@orai.eus
